PLS Analyses for Genomics

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Documentation for package ‘plsgenomics’ version 1.3-1

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Colon Gene expression data from Alon et al. (1999)
Ecoli Ecoli gene expression and connectivity data from Kao et al. (2003)
gsim GSIM for binary data
gsim.cv Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for binary data
leukemia Gene expression data from Golub et al. (1999)
mgsim GSIM for categorical data
mgsim.cv Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for categorical data
mrpls Ridge Partial Least Square for categorical data
mrpls.cv Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for categorical data
pls.lda Classification with PLS Dimension Reduction and Linear Discriminant Analysis
pls.lda.cv Determination of the number of latent components to be used for classification with PLS and LDA
pls.regression Multivariate Partial Least Squares Regression
pls.regression.cv Determination of the number of latent components to be used in PLS regression
preprocess preprocess for microarray data
rirls.spls Classification by Ridge Iteratively Reweighted Least Squares followed by Adaptive Sparse PLS regression for binary response
rirls.spls.tune Tuning parameters (ncomp, lambda.l1, lambda.ridge) for Ridge Iteratively Reweighted Least Squares followed by Adaptive Sparse PLS regression for binary response, by K-fold cross-validation
rpls Ridge Partial Least Square for binary data
rpls.cv Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for binary data
sample.bin Generates design matrix X with correlated block of covariates and a binary random reponse depening on X through logit model
sample.cont Generates design matrix X with correlated block of covariates and a continuous random reponse Y depening on X through gaussian linear model Y=XB+E
spls.adapt Classification by Ridge Iteratively Reweighted Least Squares followed by Adaptive Sparse PLS regression for binary response
spls.adapt.tune Tuning parameters (ncomp, lambda.l1) for Adaptive Sparse PLS regression for continuous response, by K-fold cross-validation
SRBCT Gene expression data from Khan et al. (2001)
TFA.estimate Prediction of Transcription Factor Activities using PLS
variable.selection Variable selection using the PLS weights